Conventional computation use processors that include circuits of millions of transistors to implement logical gates on bits of information represented by electrical signals. The architectures of conventional central processing units (CPUs) are designed for general purpose computing, but are not optimized for particular types of algorithms. Graphics processing, artificial intelligence, neural networks, and deep learning are a few examples of the types of algorithms that are computationally intensive and are not efficiently performed using a CPU. Consequently, specialized processors have been developed with architectures better-suited for particular algorithms. Graphical processing units (GPUs), for example, have a highly parallel architecture that makes them more efficient than CPUs for performing image processing and graphical manipulations. After their development for graphics processing, GPUs were also found to be more efficient than GPUs for other memory-intensive algorithms, such as neural networks and deep learning. This realization, and the increasing popularity of artificial intelligence and deep learning, lead to further research into new electrical circuit architectures that could further enhance the speed of these algorithms.